Experimental Comparison of Flaw Selection Strategies

As discussed in the previous section, several different proposals
have been made in the literature about how best to reduce the size of
the search space during POCL planning. These include:

giving preference to threats over open conditions;

giving preference only to certain kinds of threats (either separable or
forced threats), and delaying other threats until after all open
conditions have been resolved;

giving preference to flaws that have minimal repair cost;

giving preference to the most recently introduced flaws.

Moreover, different strategies have combined these preference schemes in
different ways, and apparently conflicting claims have been made
about the effects of these preferences on search-space size.

To resolve these conflicts, we performed experimental
comparisons of POCL planners using a variety of flaw selection
strategies. We gave particular attention to the comparison of LCFR
and ZLIFO, because of the their apparently conflicting claims. LCFR
generates its search space treating all flaws uniformly, using a
least-cost approach to choose among them. ZLIFO distinguishes between
flaw types (non-separable threats, open conditions, and separable
threats), and uses a modified LIFO approach to select among the flaws in each
class. The original LCFR studies would have led us to predict that
ZLIFO would generate larger search spaces than did LCFR, but Gerevini
and Schubert found just the opposite to be true. We aimed, then, to
explain this discrepancy.

Our principal focus was on search-space size, for two reasons. First,
the puzzle raised by LCFR and ZLIFO is one of space, not time. As we
mentioned earlier, it is easy to see why ZLIFO would be faster than
LCFR, even on a per node basis. A least-cost strategy must compute
repair costs, while ZLIFO need only pop a stack containing the right
type of flaws. The puzzle for us was not why ZLIFO was faster, but
why it generated smaller search spaces. Second, we believe that
understanding the effect of search control strategies on search-space
size can lead to development of approximation techniques that produce
speed-up as well; the QLCFR strategy [Joslin & Pollack, 1994] and Srinivasan
and Howe's strategies (1995) are examples of this.

However, a secondary goal was to analyze the time requirements of the
strategies we compared, and we therefore collected timing data for all
our experiments. As we discuss in Section , the strategy
that tends to generate the smallest search space achieves enough of a
reduction to pay for its own overhead, by and large.